1. Field of the Invention
The present invention relates to a neural network computation apparatus which is a parallel computation apparatus modeled on the neural network of a human being.
2. Description of the Related Art
Recently, a highly distributed computation apparatus called a neural network computation apparatus (also called neural-net or neural network) has gained the public attention.
The neural network computation apparatus is modeled on the neural network of a human being. It has the advantages of high speed and noise resistance due to its structure of parallel computation. However, the most attractive feature of the neural network computation apparatus is a learning capability.
The learning capability is a capability for accomplishing desired input and/or output characteristics only by teaching examples by using input and/or output signals to the apparatus. Applications using such characteristics have been widely attempted in many fields such as image processing, character recognition, control systems, and expert systems.
As one application of such a neural network computation apparatus, a voltage state evaluation system for a power system has been known.
FIG. 7 is a block diagram showing the structure of such a voltage state evaluation system. This system evaluates whether or not the power system is in a dangerous state with respect to stability by using a pattern of a state variable group representing the state of the power system. As the state amounts of the system, it is possible to consider voltage, passed power, phase difference angle, and so forth. However, such a system in this example only deals with the voltage.
In FIG. 7, reference numeral 101 is a power system composed of power stations and so forth. A sensor 103 detects the voltages of power stations in the power system 101 and sends the voltage pattern to a neural network computation apparatus 105.
The neural network computation apparatus 105 outputs an evaluation result in accordance with the voltage pattern sent from the sensor 103. The operator of the power system commands a control unit 107 in accordance with the evaluation result and thereby the control unit 107 controls the control system 101.
FIG. 8 is a schematic showing the structure of the neural network computation apparatus 105 in detail. As shown in the figure, the neural network computation apparatus 105 comprises 250 input units 109, 20 intermediate units 111, 12 output units 113, and 12 links 115.
The sensor 103 inputs the voltage pattern to the input unit 109. The voltage pattern is made by discreting voltages with a width of 0.01 [pu] and by meshing them with node numbers and voltage range. Although the total number of nodes is 35, since 10 nodes of power generators are excluded, 10 nodes are considered. On the other hand, since the voltage range is divided into 10 levels, 250 meshes (i.e. 25.times.10=250) are created. One neuron of the input unit 109 is assigned to each mesh.
There are 12 output units 113 which output 12 evaluation items as shown in FIG. 9.
The learning operation of the aforementioned neural network computation apparatus is executed in the following manner.
14 teaching patterns shown by the horizontal columns of FIG. 9 are made, where each evaluation item is set to 1.0 if its condition is satisfied; and it is set to 0.0 if the condition is not satisfied. The teaching operation is repeated in the order of the pattern 1 to pattern 14.
The neural network computation apparatus taught in the aforementioned manner outputs an evaluation result shown in FIG. 9 in accordance with voltage patterns given as examples.
FIG. 10 and FIG. 11 exemplify input and output characteristics of the neural network computation apparatus after the learning operation. As shown in FIG. 10, when the voltage patterns of all the nodes are lower than 1.0, which is the reference value, the evaluation item 1 "Day time", item 6 "Abnormally low", and item 12 "Overall" are satisfied and thereby an evaluation result in accordance with the pattern 4 as shown in FIG. 11 is output.
However, in the conventional neural network computation apparatus, after the learning operation is completed in a learning algorithm, the strength of the links is fixed and the determination is made in accordance with only the pattern of input data. Thus, since the neural network computation apparatus only outputs fixed and simple output patterns, it cannot make logical determinations.
In other words, the neural network computation apparatus cannot determine unknown patterns as opposed to specialists who determine matters through their experiences. Thus, the neural network computation apparatus may output improper results.
An example of an improper determination made by the aforementioned voltage state evaluation system against an unknown pattern will be described in the following.
In this example, we consider the capability of interpolation of the neural-net where the pattern 8 "Northwest region voltage drop" shown in FIG. 9 is changed and deviated and thereby the feature of the pattern 8 gradually becomes weak.
FIG. 12 is a diagram showing voltage patterns being input to the neural network computation apparatus. FIG. 13 is a diagram showing an evaluation result when the voltage patterns shown in FIG. 12 are input. FIG. 14 is a diagram showing voltage patterns being input to the neural network computation apparatus. FIG. 15 is a diagram showing an evaluation result when the voltage patterns shown in FIG. 14 are input. FIG. 16 is a diagram showing voltage patterns being input to the neural network computation apparatus. FIG. 17 is a diagram showing an evaluation result when the voltage patterns shown in FIG. 16 are input.
In FIG. 12, FIG. 14, and FIG. 16, the node numbers 24 and 25 accord with the northwest region. The voltage drop amount of the region gradually decreases in the order of that shown in FIG. 12, FIG. 14, and FIG. 16.
FIG. 13 precisely shows the output according to the pattern 8. On the other hand, in the case where voltage change and voltage deviation are small as shown in FIG. 14, if the characteristic of the overall shape of all the patterns is remarkably similar to that shown in FIG. 12, the outputs of the output unit numbers 1, 5, and 8 as shown in FIG. 15 become large and thereby the determination of the pattern 8 is made.
On the other hand, in the case where voltage change and voltage deviation of the node number 24 and the node number 25 are large and voltage drop takes place in other nodes, the outputs of the output unit number 3 and the output unit number 12 become large.
The output of the output unit number 3 represents "Healthy voltage". The output of the output unit number 12 represents "Overall". FIG. 17 is also similar to the pattern 1.
Although such a determination is not suitable in terms of the similarity of the voltage pattern shape, it is unsuitable in terms of the meaning.
When a determination is made by referring to FIG. 17, the change and deviation of the figure is closest to those of the pattern 1 except for the pattern 8. On the other hand, when the operator sees the diagram shown in FIG. 17, he or she cannot easily determine whether the change and deviation pattern of the figure is close to that of the pattern 1 or the pattern 8.
However, a specialist in the control operation of an electric power system will focus on the voltage drop in the northwest region which may result in a serious situation and the similarity with the pattern 8 even if it seems totally to be healthy.
The aforementioned determination comes with a logical interpolation as well as the similarity in terms of shape.
In other words, while a specialist feeds back his or her logical knowledge to a pattern recognition, the conventional neural network computation apparatus does not consider the relationship between the logical knowledge and the pattern recognition capability. Such an omission of the connection of the logical knowledge and the pattern recognition capability results in one of the major factors of restricting the performance of the neural network computation apparatus.
Thus, it is very important to logically make determinations of the neural network computation apparatus so that it properly determines unknown patterns.
Therefore, an object of the present invention is to solve the aforementioned problem and to provide a neural network computation apparatus which can make determinations similar to those made by specialists in accordance with logical knowledge.